On today’s show, we chat with Gregory LaBlanc, a lecturer and Distinguished Teaching Fellow at the Haas Business school at the University of California Berkeley. He teaches primarily in the areas of finance and strategy in the MBA and Master of Financial Engineering programs, and in Executive Education. His research interests lie at the intersection of law, finance, and psychology, in the area of business strategy and risk management.
In this episode, you’ll learn:
- What areas in finance will be affected by artificial intelligence?
- What is “behavior in trading” and how will machines impact this?
- Who are the biggest global players in Fintech?
- What is the future for MBA programs?
- What is Fintech School and what can you expect from it?
We would like to give a special thanks to My Nguyen who is the founder of Fintech School for connecting us with Gregory La Blanc. Without My, this interview would not have been possible.
Help us out!
Help us reach new listeners by leaving us a rating and review! It takes less than 30 seconds and really helps our show grow, which allows us to bring on even better guests for you all! Thank you – we really appreciate it!
Books and Resources
- Gregory LaBlanc’s LinkedIn
- Gregory LaBlanc at Berkeley Business School
- Check out Fintech School
CONNECT WITH SHAWN:
https://linktr.ee/ShawnflynnSV
- Shawn Flynn’s Twitter Account
- Shawn Flynn’s LinkedIn Account
- Silicon Valley LinkedIn Group Account
- Shawn Flynn’s Facebook Account
- Email Shawn@thesiliconvalleypodcast.com
Disclaimer to the Transcripts:
With the use of Artificial Intelligence to generate this Transcription; we would like to thank you in advance for understanding that there might be some inaccuracies. While reading, one might also notice that there are times were the sentences are not grammatically correct and due to changes in advertisements, the time stamps do not always align with the show. We are keeping the text as true to the interview as possible and hope that the transcript can be used for a reference in conjunction with the Podcast audio. Thank you and enjoy.
Intro 00:00
You’re listening to The Silicon Valley Podcast.
Shawn Flynn 00:02
On today’s show, we have Gregory LaBlanc, who is a lecturer and distinguished teaching fellow at the Haas Business School at the University of California-Berkeley. He teaches primarily in the areas of finance and strategy in the MBA and Master of Finance Engineering programs, and in executive education. His research interests lie at the intersection of law, finance, psychology, and in the areas of business strategy and risk management. In this episode, we talked about what areas of finance will be affected by artificial intelligence, what is behavior in trading, and how will machines impact this, what is the future for MBA programs, FinTech school, and the key players in FinTech. So, stay tuned for an amazing episode.
Intro 00:49
Welcome to the Silicon Valley Podcast with your host Shawn Flynn who interviews famous Entrepreneurs, Venture Capitalists and Leaders in Tech. Learn their secrets and see tomorrow’s world today.
Shawn Flynn 01:13
Gregory, thank you for taking the time today to be on Silicon Valley. Now, you’re the founder of Berkeley FinTech Institute, a distinguished teacher fellow at UC Berkeley Haas School of Business. You have this amazing background. What led you to become so fascinated with financial technology?
Gregory LaBlanc 01:29
Well, actually, it goes as far back as my graduate studies. I actually worked on a PhD in financial history, which sounds kind of esoteric, but it really was about how markets and institutions evolve over time, historically. Then you’re in a good position to kind of look at how they evolve over time and in real time, and you have a pretty good insight into what are the forces that create innovation and lead to changes in markets and institutions.
Shawn Flynn 01:53
So then, what patterns are you seeing right now from history?
Gregory LaBlanc 01:57
They say there’s nothing new in history, right? What happens is that the cost of information change, the cost of organization change. Obviously, technology is always evolving. But as technology changes, it really impacts key variables, like cost of transacting, cost of contracting, cost of screening, cost of scoring. These are things that if you understand those trends and those forces on a larger scale, then now it’s pretty easy to understand how they happen in real time.
Shawn Flynn 02:25
So does the introduction of artificial intelligence affect any of these trends? Or how’s that making the playing field now different?
Gregory LaBlanc 02:34
Yeah, absolutely. I mean, when we talk about artificial intelligence, machine learning, you know, big data, I mean, really, what we’re talking about is a reduction in information costs, right? We have a reduction in the cost of data capture, reduction in the cost of data storage, reduction in the cost of data analytics, right?
And much quicker response time to the information as it comes in. And so, finance is really all about information. The entire industry is information economics and financial economics. They overlapped to quite a degree. So, as you reduce these costs of information, that enables a whole bunch of decisions to be made differently. And so, machine learning is really, you know, just improving on the decision making that people have been trying to do for decades, for centuries, for millennia.
Shawn Flynn 03:19
So then how does this affect the different areas in finance?
Gregory LaBlanc 03:23
Is there an area of finance that is not affected by machine learning? And the answer is probably not. I mean, okay, the couches in the waiting room in the few remaining branches, you know, may not be affected by machine learning. Although, I’d probably argue they would, they probably can do some studies to find out like the configuration of couches that will optimize, you know, the conversion rate of new customers waiting in those branches. But really, whether you’re talking about lending, whether you’re talking about insurance, whether you’re talking about brokerage, whether you’re talking about asset management, you’re talking about fund origination. There are payments, I mean, there’s really no aspect of finance that is not factored in a really profound way by artificial intelligence.
Shawn Flynn 04:02
So, can we dive deeper into one of those areas? Maybe banks, process automation? How will that be different?
Gregory LaBlanc 04:08
You know, every decision is really, as a data scientist, you look at every decision, and you see it either as some kind of classification or some kind of regression, right? You’re classifying customers into those that you should lend to and those that you shouldn’t lend to, right? Or, you know, those that you should increase interest rates on or reducing interest rates on, or you should solicit big customers or not. And so, all of those decisions are improved with access to better information and the ability to analyze that information more effectively and more quickly. The classic problems in lending and insurance, adverse selection and moral hazard, are really fundamentally informational problems, trying to predict ahead of time who’s going to default, trying to predict ahead of time who’s going to prepay… Let’s say if prepayment is a risk you’re worried about, trying to predict who’s going to borrow. These are all predictions that can be improved by having more rows and more columns in your database, and by having good solid analytic capabilities.
Shawn Flynn 05:05
So, you’d mentioned making credit decisions? How are the banks adjusting that now with the new information?
Gregory LaBlanc 05:11
Well, I think it would be hard to find a financial institution that is not incorporating new kinds of data. Obviously, the mainstream banks, commercial banks, they’re highly regulated. Every lending institution is regulated by the Fair Credit Reporting Act. And every bank is regulated by the OCC. And so, you know, there are a lot of features that the banks are restricted from using, like race, ethnicity, national origin, religion, and even location. There are a lot of things that we can’t look at. But these things that we think of as alternative data are being increasingly introduced into the credit decisions. I mean, in the past, I think banks would more or less coalesce around a single metric, like the FICO score. And that’s why bankers were widely ridiculed. I mean, there’s the whole 3-6-3 rule, right? You know, you borrow at three, lend at six. You’re on the golf course, by three o’clock, because you know, you don’t have to do anything. You just followed a pretty established rule. Maybe I’m exaggerating a little bit.
Gregory LaBlanc 06:07
But you know, now banks are increasingly competing for customers. And I would say that it’s not just about identifying credit risk. It’s not just about looking at things. I mean, there are some famous examples of lenders that are looking at esoteric data, like your battery usage, right? So, it’s pretty well-known now that some companies will look on your phone and at all sorts of things. I mean, there’s so much on your phone, like what apps you have on your phone, and you know, when do you wake up? And when do you go to sleep? And where do you go during the daytime. I mean, you go to the same place every day, you go to casinos. There’s so much that you can look at just from sucking phone data. Or people who have no paper trail. If you’re unbanked and you have no employment history, it’s on paper, or you have no credit history or checking history, we can still extract all sorts of features just from your phone. The most famous one is that everyone just talks about is if you let your battery drain all the way down to zero, and then you go and recharge it, that is correlated with a lower probability of repaying, right? In other examples or Prosper asks all of the applicants to write a little essay. Leave me alone because I’m broke, right? And you can just do some simple Naive Bayes type analytics, just some text analytics on those essays. And it’ll alter your lending decision, because there are correlations there. Now, of course, Prosper is the only one that has that database. And so, they, even though any other lender would love to have that, they don’t have five, six to seven years of little essays that they can match with the repayments.
Gregory LaBlanc 07:33
So yeah, everybody’s looking for that special little piece of data. And it need not be esoteric. I mean, it can be like, you know, your rent check, right? Like, oh, let’s look at your rent. Do you pay your rent? Do you pay rent on time? Do you pay the electricity bill on time? I mean, FICO score doesn’t even include your salary, right? So why don’t you include your salary when you’re making a credit? So, there’s lots of things that have informational value. And I would say also that, you know, not just credit decisions, it’s what do you cross market to? You’ve got somebody who’s a checking account customer, should you market a mortgage to them? The FinTech startups that are in a pure play space, right? If you’re trying to make money by refinancing student loans, okay? You know, you’re not going to make it because even if you do it better than anybody else, even if you have the best model, you’ve got more rows and columns than anybody else, customer acquisition costs are so high that you’re just never gonna be able to monetize it. It’s such a competitive thin margin space. And this is why the Wells Fargos of the world make money. You get somebody in the door for a student loan, it’s a, “Okay, should I pitch a mortgage to these guys? Should I pitch some investment products to them?” I mean, this is how, if you look at Alibaba, right, you look at Edfinancial, you look at WeChat, they are just building out these financial powerhouses, you know, with the financial superstore, the financial supermarket, because once you have a customer in the door, and you start capturing all this data about them, then you might as well just figure out how to market to them all sorts other products. And you don’t want to just to market everybody, everything to everybody, because then they become numb to your ads. You want to be very careful about how you do your cross marketing.
Shawn Flynn 09:01
So, with all this information, how is there still broad and anti-money laundering, shouldn’t that have all been eliminated?
Gregory LaBlanc 09:09
Yeah, I mean, that’s a really, really good question. So, there are a couple of issues here. First of all, I would say that fraud detection algorithms are quite good. Some of the best minds in the business are devoted to fraud detection. And so, you know, there are companies that I think are their basic competitive advantages are fraud detection, right? If we look at Visa for instance, or we look at PayPal, I mean, what do they really add to the mix, at the end of the day? I mean, those Libra takes off, right? Why would you need Visa? Why would Visa join the Libra consortium, right? It makes no sense. Isn’t it just going to put them out of business? Well, companies like Visa, you can think of them as fraud protection as a service. That’s what they’re really good at. They are really good at detecting fraud.
PayPal is really, really good at detecting fraud. Now, you’re still going to see plenty of fraud. And that’s because fraud is a very low probability event, right? I market mortgages to my customers, and you know, 20% of them respond. Okay, that’s actually, you know, I can develop a nice rich model. If one out of a million transactions are fraudulent, it’s really, really hard to avoid having a gazillion false positives. It’s really hard to come up with an accurate model. I mean, even if you flag every single fraudulent event, you’re also going to catch in that net a whole bunch of trash fish, right? You’re just going to catch a ton of… And so, every single person is going to be, if you want to catch every single instance of fraud, and you’re pretty much going to be telling every… You know, every traveler is going to find that their credit cards are not going to work. So, you have to actually let some fraud through, realistically, in order to avoid all these false alarms. So that’s number one. It’s a very low probability event. It’s like scoring in soccer, right? Like, it’s really hard to do analytics on soccer, because nobody ever scores. You know, baseball’s easier because people actually hit the ball once in a while.
Gregory LaBlanc 10:58
The second thing is that it’s an arms race. We got really intelligent people who are out there trying to do for a hothead, and you got really intelligent people out there trying to stop it. And so, it is an arms race. It’s a cat and dog game. And so, you have to continually be revising your fraud models. And so, I remember I’ve talked to a lot of machine learning people who go from stable environments, like voice recognition, and then they go to work at hedge funds, or they work in fraud detection. And they realize that it’s a lot harder problem. I recently went out, I spoke at a hedge fund, a quant fund, and had some of the best machine learning people in the world. You know, voice recognition people, you know, astronomers looking for stars and whatnot. They’re like, “This is crazy. The minute we detect a pattern, it goes away, it disappears. It’s like, well, what’s going on here?” It’s like, yeah, voice recognition… People aren’t changing the way they talk in order to outsmart Alexa, like they want Alexa to recognize their voice. Whereas if you’re a trader in the financial markets, everybody’s trying to disrupt… You find a pattern, you start making money on it, everybody else is making money on it, and then the complex adaptive system responds, opportunity goes away. So, everybody’s constantly in motion. Same thing with fraud. The minute you have figured it out, they’ve moved on to the next thing.
Gregory LaBlanc 12:15
And then the third thing is that the attack surface is really huge. I mean, imagine that you’re defending a city, and somebody could break through the city walls with a slingshot. Like, where do you put your defense? You can’t defend the entire perimeter, when the cost of destroying something is a tiny fraction of the cost of protecting something, something’s got to give. And there’s this NotPetya virus. I don’t know if this is, you know, well-known… But this is a virus that the Russians sent out to the Ukraine, basically to disrupt all the servers in the Ukraine. Any company it was operating in the Ukraine was affected. Maersk, the international shipping company, had to take a $600 million write-off, because they were affected. What did it cost Russia? I don’t know. But it probably costs like 100,000 bucks to come up with this virus. And it costs 100 billion dollars in damage. That’s the issue. I think with things like fraud and hacking, we’re going to have to invest an enormous amount, we have to spend a lot more on defense than is being spent on offense.
Shawn Flynn 13:15
You just mentioned Libra, the Facebook coin, I believe. Can you talk about that?
Gregory LaBlanc 13:21
I’m personally very excited about this. You know, in the United States, we’re not really super familiar with mobile payments, even though we have all the infrastructure. We have Apple Pay, we have Android Pay, we have Samsung Pay, we have all this stuff. And yet, the number one mobile payment system in the US is Starbucks. This is crazy. It’s nuts. But you got other parts of the world and mobile payment, it has a lot of penetration. I mean, particularly in China, mobile payment is the norm. Everybody uses mobile payment devices. So, one of the reasons why it hasn’t penetrated the US and also hasn’t penetrated, frankly, to most of the developing world… You know, in the US, we pretty much already have POSs on the merchant side for credit cards.
Most people have credit cards, it’s not really a solution to an existing problem. In the developing world, it actually is a real problem because people don’t have credit card. In parts of Africa, you might have 10% to 20% of the population that doesn’t even have a bank account. Okay, so that’s a huge problem. So, the one hand, you’ve got Ali and Tencent that are trying to make a play into these markets. On the other hand, you have the mobile phone operators, like MTN and Vodafone who are trying to make a play into this space. And then comes Facebook and its consortium with Libra. I think this offers a huge, huge… Satisfies a huge need for these unbanked populations. To a lesser extent it solves the problem in the US. Really the only way that you could see something like a WeChat pay or AliPay penetrate, is you have to have a head start. You have to have the chicken and egg problem. You need receivers and payers. WhatsApp is really one of the few entities that has the global presence on both the payer and the payee side that would enable like instantaneous adoption. So that’s why there’s I think there’s a massive, massive potential here for building out a mobile payment’s ecosystem on the basis of Libra.
Shawn Flynn 15:08
What do you think Western governments’ reactions will be?
Gregory LaBlanc 15:12
Yeah, we’ve seen some pushback in the last couple weeks from US Congress and the regulators, and so forth. But you know, at the end of the day, it’s not really clear what they can do, it’s not clear that they have the capability to really just step in and apply instant regulation. And that’s because this is not a bank. And so, the banking regulation doesn’t apply. There’s talk that it might be considered a security associate… FTC has suggested that it might be a security. And that’s really interesting, because if it was a simple, stable coin, and it was backed by the US dollar, then it would not really be a security, because the SEC has already come out and said that if something has general utility as a medium of exchange, then it will not, like Bitcoin, it will not be considered a security, but because it has a basket. It’s kind of like a derivative, right? And the FTC regulates these baskets. So, see, FTC is kind of exploring stepping into the mix. There are also some tax issues that we might have to think about, because it’s not linked to the dollar, but to a basket of currency, then it will fluctuate in value relative to the dollar. There could be some tax consequences.
But at the end of day, it’s really just a contractual relationship that they have with the customer. It might actually be the FTC that is in charge of regulating this thing. There is a lot of uncertainty around it. And you know, if the politicians really wanted to kind of mess with it, they could. But at the end of the day, I don’t think that the US is the market where there’s the biggest opportunity here. I really think that the biggest opportunity is in the emerging markets. And from the perspective of the emerging markets, yeah, they’re probably interested in regulating it. But when you compare the solution that people are currently using, which is cash, which is completely invisible to these governments, not taxable, it’s not auditable, it’s completely invisible.
This actually offers the possibility of more visibility, depending on how it is implemented, then all these transactions will be auditable. There’ll be you know, there’s an AML protection. For those governments in West Africa, I think they’ll look at this and say, “You know, this is not replacing the regulated financial transactions that we have in the banks. This is replacing the already unregulated invisible transactions that happen through cash.” And so, I think they’ll actually be very permissive in terms of their regulation. My forecast might be wrong. Wrong, more often than right. But that’s definitely worth the gamble.
Shawn Flynn 17:41
With that being said, and maybe the government’s actually encouraging this, could it almost be seen as a monopoly with currency?
Gregory LaBlanc 17:49
Well, I mean, obviously, it will be competing with other mediums of exchange. So, in that sense, I don’t think it would be seen as a monopoly. Now if it gets rapid adoption, then yes. It could start to dominate small and medium businesses. But at the end of the day, in China, you’ve got WeChat Pay and AliPay, and they’re more or less like a duopoly, right? But the competition keeps them in check, I think. The other thing is that this Libra platform is open. Anybody can build out applications on it the way it’s set up. The Calibra Wallet that Facebook is offering is only one of, you know, any number of wallets that could be built out on this platform. That’s number one. And then number two, the idea is that this is going to be a consortium.
And they already have 28 nodes, and they’re talking about having 100 nodes. So, Facebook will not have any kind of control over validation of these transactions. And Facebook won’t have any visibility into the transactions that occur outside of the Calibra wallet. So, I think it was actually built to bake in some competition, precisely so that it would avoid any accusations of monopoly. Now, of course if all the transactions happen within Calibra, then it will be seen as a monopoly. But like Facebook, it doesn’t have any competitors in social media, but it will have plenty of competitors in the world of transaction media.
Shawn Flynn 19:09
Going back to trading algorithms right now, how are they impacting the financial markets?
Gregory LaBlanc 19:15
Look, whenever I see a human trader, I’m actually kind of surprised, like seeing somebody, you know, show up in the 711 on a horse is, you know, human trading is really going the way of the dinosaur. There’s already more algorithmic trading going on now than human trading. And when we would think of program trading in the past, you know, a lot of it was just automated rebalancing. Until a lot of the stuff that the robo advisors are doing is just automated rebalancing. And then, a lot of hedge funds are also, you know, you stick out a position where you have a whole bunch of automatic rebalancing, keep your portfolio in check, okay. But, you know, we also have these active algorithmic traders that are just continually looking for alpha, just constantly searching for alpha. And then, you know, high-frequency traders, like all they’re doing is just trying to create liquidity, right? Trying to anticipate trades, and facilitate trades, and so forth. So, I think it’s already at the point where most of the trading is being done through some kind of algorithm.
Gregory LaBlanc 20:08
On the bigger, more philosophical question is, does all of this automation of trading kind of create more liquidity or less liquidity? Does it create more stability or less stability? The answer is it depends. And it really does depend on the composition of algorithms, the ecosystem of algorithms. It is one of the more fascinating areas of finance. And it dovetails a lot with, you know, biology and other complex adaptive systems because if I have a bunch of momentum traders, let’s say, you know, when something goes up, you buy it, right? And then you’ve got a bunch of contrarian traders, whenever something goes up, you sell it. Well, if I’m trying to forecast what’s going to happen, after price movement, or an informational signal, I need to know how many momentum traders are out there. And how many contrarian traders are out there. And my choice of whether to become one or the other, is going to depend on what I think the ecosystem looks like. So, you can have these systems either reinforcing each other or kind of counterbalancing each other. Now, of course, this was true when humans were doing the same thing. So, we saw bubbles, I mean, no one’s going to say that bubbles are a new thing, I mean, and that algorithms are going to lead to bigger bubbles… It can’t get any bigger than the, .com bubble, and so forth. But I think that things will happen a lot faster. So, the flash crashes can occur in the blink of an eye. Whereas with the humans, there was like a delay. And that can either be, you know, better for worse.
Shawn Flynn 21:35
So online, I watched the video that you did on behavior in trading, wouldn’t that be completely eliminated if it went all to robots?
Gregory LaBlanc 21:41
That’s a really good question. Because I think that well, on two levels. On the main level, which has to do with correlation of trading behavior, which leads to bubbles, you know, algorithms do the same thing. I mean, humans make decisions using algorithms, Let’s be clear. It’s not like algorithms are replacing non-algorithms. Humans use algorithms. So, you know, when a human says, “When the stock price goes up, I buy it.” Okay, and we replaced that carbon-based algorithm with a silicon-based algorithm that says exactly the same thing, “When the stock goes up, buy it.” You get the same results. So, behavior is not going to be eliminated, because behavior is just algorithms. And then humans adapt, and humans adjust, and algorithms adapt, and algorithms adjust. I teach a course called behavioral finance.
Of course, it is really about complex systems. It’s really about feedback loops. It’s about information and how information is disseminated and how information impacts activity in the markets. And that activity could be done by humans or could be done by robots. it really doesn’t matter. And so, I think what we call behavioral economics is just as relevant now, even though all the decisions are being made by robots. We just have to understand robot behavior instead of human behavior.
Gregory LaBlanc 22:51
Now, at the retail level, this is where I spent a lot of time with financial advisors, and financial advisors, you know, they say, “These robo advisors, they’ll never put us out of work. They’ll never put us out of work because humans like to talk to humans. They really like to talk to a human and only a human can talk someone off the cliff, when they’re about to sell everything in 2008, right?” Kind of like medicine, right? “Oh, well, doctors can just redirect the doctors to holding people’s hands. So, when they get diagnosed with cancer, nobody wants to be diagnosed by a robot, right? Maybe a robot diagnosis. And but then you got the human that carefully explains them, tells them you know don’t worry or whatever.” I used to believe this. And I used to think that this was plausible that you’d have this human interface. And then everything would happen behind the scenes are just robots.
But the more I look into the way financial advisors operate, they don’t really have much business providing advice to humans. They’re almost as irrational as the humans themselves, right? It’s like, the doctor comes in, and smokes pack of cigarettes and says, “You got cancer, right?” So, I actually think that even on the robo advisor phase, we’re going to see more and more, even that hand holding thing is going to be done more and more by robots, right? I mean, there’s actually some robo therapists, and they’ve been determined to be, you know, equally effective as humans. So, you know, you’ve got a chatbot you say, “Hey. You know, there’ll be some things that Alexa… Alexa, I’m feeling pretty sad.” And Alexa will be like, “Oh, well, you know, tell me about your childhood, right?” And, you know, psychologists use algorithms in their own head. I mean, I’m being a little facetious here. I do think that obviously, the human empathic capability will never be replaced for a lot of things that humans are replicable.
Shawn Flynn 24:34
So, what do you think most of these humans, these day traders that…? What do you think their next goal or path will take them then?
Gregory LaBlanc 24:41
Creativity and strategic thinking, machine learning still has ways to go. When I talked to those folks out in the hedge fund, we’re having trouble understanding why their pattern recognition skills didn’t work. They had to start thinking a little more strategically. They had to put themselves in the minds of the other traders, and they had to really use a little bit of game theory and thinking at a little more complex level about what’s going on now. But Big Blue has shown us that beers can think strategically. But that’s it, that’s in a finite choice space. It’s a finite set of rules. Well, pretty huge choice space, right? But, you know, computers now can teach each other how to play go just by playing against themselves. Computers can think strategically. But financial markets are unbounded. Financial markets have lots of different things going on, including lots of human traders that are still involved.
Humans still set monetary policy, humans still elect other humans to make decisions that impact the real economy and signals because the world is changing and innovating. You know, historical data doesn’t always help because things are not stable. I need to let a robot evaluate Uber when it goes IPO. I’ve had a lot of students who have tried to come up with machine learning models that will help you as a venture capitalist. Okay, and there are a couple of venture capital firms that are using, you know, algorithms to try and evaluate teams and business ideas. And I say good luck to that. Machine learning works on stable patterns with good historical data. And when we’re dealing with innovation, and the financial markets in the US are all about innovation, I really don’t think that there’s no machine learning model known demand that would tell you buy Amazon stock in 1996. It is not going to tell you, because there’s nothing to compare it to. There’s no training data that would give you a good score on Amazon, or on Facebook, or on Tesla, or on Uber. And so, humans are always going to play a huge role in finance, in my view.
Shawn Flynn 26:37
So, you did mention that you had several MBA students try to use financial modeling to predict Uber. What do you think is the future for MBA programs?
Gregory LaBlanc 26:46
That’s a question that might get me in trouble. I have a colleague who says that if you want to learn how to run a 19th century railroad, go get an MBA. So, I gotta stick up for the MBA a little bit, because that’s how I make my living. Obviously, there are some MBA programs that are better than others. And I’m not going to flog mine, but I think ours is among the better. That said, you know, MBA programs are… They are in crisis. I mean, a lot of lower ranked MBA programs are losing students, and they’re losing money. And part of that is because the curriculum is not adapting. Now, look, MBA programs, bring you think you’ve got the network, you’ve got the branding, and you’ve got the curriculum. And the network and the branding are always going to be powerful. But if the curriculum doesn’t change, then these programs will go obsolete, you’ll get a financial engineering program.
For instance, Berkeley, our financial engineering program was historically all about pricing derivatives, pricing finance, complex financial instruments. And then after the financial crisis, the recruiters told us they said, “We want data science. We want people to understand data, understand to find patterns in data.” Okay, so we had to change our curriculum substantially. You know, the mainstream MBA program, same thing. Students want to have the ability to communicate with technical people, particularly here in the Bay Area. And if you’re an MBA, you don’t have just baseline, understanding how to manage teams of engineers, how to recruit engineers, and how to talk to engineers. There are some people that say, “Oh, I just need to go and learn Python.” I think that’s actually wrong. I mean, that’s not what MBAs are for. MBAs are supposed to be PhDs in common sense. They’re supposed to be generalists. They’re supposed to know, not how to program in Python, not how to do the accounting on some complex financial transaction, not understand the legal ramifications of different SPVs, and so forth, but understand the relevance of that, understand how to put the pieces together to understand how to create organizations that can exploit opportunities that are available in the marketplace.
Gregory LaBlanc 28:37
So, the main changes I think that MBA programs have to offer are understanding how to manage technology, understanding a bit about how that technology works, what are the limitations and possibilities of that technology, whether we’re talking about machine learning and artificial intelligence. You know, whether we’re talking about the Internet of Things, whether we’re talking about things like distributed ledgers, you need to know a bit about that. The other thing I would say is that the way in which companies are organized, it’s completely different. Talk a lot about the compostable enterprise and building around APIs. And part of this is technical. Part of it really is organizational. It’s about how do you make decisions more quickly? How do you organize people into smaller teams? How do you go out and source the data that you need? How do you interface with other entities and ecosystem? How do you build out a platform type business model with multiple sides of customers? So, these are things that MBAs are uniquely, I think, suited to understand better than the technical people. So, if they try to become secondary technical people, that’s not enough. It’s not going to work. Be first rate businesspeople. But that means understanding how business has changed. And also, being able to anticipate how business is going to change. And mainly because you cannot, in today’s world, acquire any kind of expertise, and expect that expertise to be useful. 18 months later, you go to medical school and learn medicine, and then you expect to coast on that knowledge for 40 years, not going to work. So, you have to focus on being adaptable, being flexible, being able to learn. You have to install a really robust operating system, so that as new apps come on board, they seamlessly integrate with this operating system that you’ve installed. That’s what MBA programs need to focus on.
Shawn Flynn 30:24
Who are the big players right now globally in FinTech? Is it banks or is it someone else?
Gregory LaBlanc 30:29
Prior to the financial crisis, the banks were top recruiters at most MBA programs. I mean, I went to Wharton and when I was in school, you got to deal with Goldman. You know, you’re set for life. Go to Morgan Stanley, JP Morgan, like, everyone knew the big banks, big investment banks, and then there were some boutiques, Wasserstein Perella, and like these guys, everybody knew who they were. That was true even up through the years before the financial crisis. Even though the tech companies, Googles and the Amazons, were starting to heavily recruit the same type of students, but people still went into finance. You know, you knew what you’re doing. BlackRock was starting to get big.
Those people who were still interested in finance, FinTech kind of became something that was interesting to them. And for me, I was encouraging students to pursue FinTech careers, because most of the FinTech were actually started by engineers. And engineers had very little knowledge of how finance worked. And so, if you had enough background on finance plus a little bit of technical expertise, it really adds a lot of value. And I think that’s what we’ve seen, you know, in the last couple years is that the top people in FinTech, they’re much more balanced now, between the people that have business backgrounds and people with technical backgrounds. So, the major players, of course, it’s combination.
I remember when I would go to innovate in these other conferences, back in 13, 14, and so forth, everyone got up and said, “We’re going to kill the banks, we’re going to crush the banks, we’re going to destroy the banks, we’re going to make the banks useless. We’re going to make the banks obsolete.” Okay, you know, this. Banking is the most regulated industry in the world, like it is more regulated than education. It’s more regulated than healthcare. It is essentially an extension of the state. I mean, it is so regulated, particularly after Dodd Frank, and you know, everything else. So, the banks aren’t going to go away any time soon. It’s more realistic to think about how FinTech and banks can work together. And so, whether it’s a FinTech that is sitting on the back end of the bank, it’s providing them with better IT stack, or providing better customer experience or whatever, you know, that’s a huge opportunity. Or doing things that are completely different from banking.
Gregory LaBlanc 32:31
But you look at for instance, robo advising, we saw betterment, we saw a personal capital, we saw wealth front. Now, how much market share do they have, at the end of the day? Nothing. Combined, it makes up like 1% of assets under management. And yet robo advisors are huge. So, I think that although banks are pretty slow, a lot of large financial players are, you know, they have enough runway that they can start absorbing and adopting a lot of these new technologies. You know, that said, of course, I still believe that Facebook, Amazon have a lot of potential. A great example is PayPal. When PayPal was created, the idea was, “Hey, we don’t need banks, we don’t need Visa, MasterCard, right? We don’t need anybody. We were PayPal. And we’re a closed loop system. Everybody can open a PayPal wallet. Everybody can pay peer to peer with PayPal. Just forget about everybody else.”
And it was not taking them to the promised land. That just wasn’t working out because people didn’t see the value proposition. Why would I do this? What I have works, okay, there’s no reason to do this. And so, PayPal made a strategic shift, where they essentially became an API company, where they said, “Hey, we’re going to play ball with all these people. We’re going to partner with Visa. We’re going to partner with MasterCard. We’re going to partner with the banks, we’re going to partner with vendors. So, we’re going to say to the vendors, you know, hey, use us and you can accept Visa. You can accept MasterCard; you can accept anything you want. And we will provide this white label solution for you on the back end, make your life easier.” And now, PayPal I think is doing great. Now PayPal is joining the Libra Consortium. You might think it’s suicide, but it’s actually an extension of that strategy of working together as part of this ecosystem.
Shawn Flynn 33:27
I often hear about blockchain. What are the basics of it? And will this have an impact in the financial industry?
Gregory LaBlanc 34:16
Yes, it will have a massive impact in the financial industry, although not in ways that most people will recognize. Okay, so I like to say, if, for some reason, the water that came out of your faucet was going through copper pipes one day, and the next day was going through PVC pipes, you never know. Or, you know, went from the Hetch Hetchy to you know, some other place, you’d never know because all that plumbing is invisible to the typical person. If you look at the way in which you buy and sell stocks right now, you look at your account, you look at the prices, and you click on it, boom, you bought some shares. But what’s the most difficult, share owners don’t realize is there’s this enormous machinery behind the scenes. It employs millions of people. You’ve got the brokers, you’ve got the exchanges, you get the clearing houses, the custodians, get the transfer agents. You’ve got all these entities. A lot of them are companies that you’ve never heard of. And when you look at international money transactions, you know that money wind up going through like four layers of correspondent banking, for that money to get from you to someone in China or whatever.
But you don’t know that. You just push a button, and something happens. All of that I think is going to be disrupted, all of the back-office stuff. And that’s going to be because of distributed ledgers. Like we could quibble about how much of this is blockchain, how much is not blockchain. I think the most exciting stuff that’s happening as a result of this new attention to blockchain is really a new attention to databases and how databases are organized, how information is shared across different institutions. And I would say that, you know, a lot of the stuff, a lot of the hype around blockchain is misallocated, because many of the things that people say blockchain can do, can be done through a really good API architecture, without the need for any of the extra baggage of blockchain like validation. But nonetheless, whether it’s blockchain or distributed ledger technology, or just a better API architecture, you know, like open banking, we’re going to see radical disruption of the core infrastructure that sits behind most of the financial transactions in the world.
Gregory LaBlanc 36:29
If you look at something like derivatives, a notional principle of all the derivatives outstanding in the world is about $800 trillion, which is eight times global GNP or something like this. I’m not sure what the number is. And most people have no clue. They don’t even know this stuff is happening. It’s like interest rate swaps, currency swaps, and stuff like that. Now, the infrastructure for that is actually pretty primitive. Until Dodd Frank, I mean, most of this stuff was over the counter. The reason why the Lehman Brothers was such a disaster is because of the massive amount of over-the-counter derivatives that they had. All these different counter parties, it all had to be taken apart. And it took… They’re still working on it. I mean, it’s like 11 years later, and they’re still trying to disentangle all these different relationships, not only relationships between Lehman Brothers and counterparty, but relationships between different parts of Lehman Brothers. Yeah, Lehman Brothers had like, I don’t know, 200 subs. And then gazillion SPVs in it. And it’s just, I mean, it’s chaos. And I think the legal bill right now is sitting at $71 billion, all going to bankruptcy lawyers. Being a bankruptcy lawyer is a nice gig, you know, you profit off expensive, all these poor miserable people, it’s really kind of kind of nice, nice job. And you know, people will always be going bankrupt. So, you’re never going to be out of work. But like, that’s crazy to me. And I think that we have a lot of opportunities for us to reorganize how we store our data that will create greater transparency, lower latency in financial transactions, and this is actually going to be… at hopeful for regulators is regulators will have better ability to see into what’s out there. So yes, it’s going to have a massive impact. But in our everyday lives, we’re never going to notice that everything will be more liquid, and it’ll be less costly to engage in all these activities.
Shawn Flynn 38:17
What’s the future for international money transfers? How are businesses from countries going to be communicated?
Gregory LaBlanc 38:24
Well, I think you asked the right question, which is, you know, business-to-business money transfers. I know when blockchain came out, a lot of people were hyping the lower costs associated with remittances, peer-to-peer money transfers. You know, remittances, if you are socially aware, remittances are something… their big pain point, you’re a migrant worker in the Arab world or in Southern Africa, or, you know, even the United States, getting money back to your relatives is fairly expensive. At the end of the day, it eats into your pocketbook. But again, today, that’s not really a big enough problem to warrant a huge amount of investment. Because in the few markets where we have, the bigger the markets like US to Mexico, US to Philippines, the fees are not that great, things like transfer wise, like that problem is being solved, not a big deal. Furthermore, it’s actually kind of problematic, because people still have to spend money in their own currency. They can’t spend these cryptocurrencies, at least now until Facebook… You know, Libra might actually help a lot with that. The amount of money that’s transferred peer-to-peer is tiny, compared to the amount of money that’s transferred from large enterprises, other large enterprises, or even small enterprises. You know, large enterprises, across national boundaries…
Gregory LaBlanc 39:32
The problem with that is, it means the same problem that you have with peer-to-peer is that, you know, nobody’s moving big barrels of cash, right? Nobody’s moving money, right? What you have are just accounting entries, it’s just debits and credits. Now, the problem with debits and credits is that, you know, I need to actually have a relationship with you already, in order to do debits and credits. I need to have, you need to have an account with me or I need to have an account with you in order for us to transfer value to one another, or we have to have an account with some third party. And that third party can facilitate a transfer value from you to me.
So, you know, money transfer is just about a series of interlocking balance sheets, and then a sequence of debit and credit transactions. And so, the longer the chain, like the larger the number of balance sheets that you have, connecting from the sender to the recipient, the higher the cost, the longer it takes, because a lot of entities use kind of different debt settlement, the higher the likelihood that there’s going to be something that goes wrong. So, a lot of the new distributed ledger technologies are designed to abbreviate that chain. And to reduce the need to have these interlocking balance sheets. You know, of course, JP Morgan has their JP Morgan coin. All of these are attempts to reduce the friction associated with international money transfer. And there is no federal reserve that goes across all these different currency regions. These are real solutions that can make life easier.
Gregory LaBlanc 41:02
Now, I should say that there are some misconceptions, because when you look at Ripple. Ripple talks a lot about XRP, which is the coin, and the idea is that the sender will buy a coin, ship the coin, but the recipient will sell the coin into local currency, and then that coin will just, you know, kind of circulate around and back and forth. Well, it turns out most of the transactions that Ripple facilitates are not actually using that coin. Instead, they are simply… it’s just a more efficient way of messaging and a more efficient way of connecting these balance sheets with each other. So, in that sense, it’s less revolutionary than you might think. But it’s still adding huge value. And it’s disintermediated the big players in the space, and so they’re not too happy about it, because banks make money from having access to better information than anybody else, information that they had was about two sides of a deal, two sides of a deal can meet in the middle without the intermediary. You know, they both benefit and the intermediary suffers.
Gregory LaBlanc 42:02
So, I think there’s some big opportunities here. Now look, it’s not going to affect the US-China relationship all that much, because governments still regulate currency flows. And that’s not going to stop. So, if you want to change RMB into anything else, you can’t do it without the Chinese government’s permission. They’re standing at the interface of every Chinese bank and the rest of the world. So, they can, once things go through the banking system, that’s when the regulators can step in. Right now, the easiest way, if you want to convert RMB to dollars, you don’t buy Bitcoin, and then rip the Bitcoin of dollars, because that’s very difficult to do.
You buy some mining machines, okay, because that’s not regulated. Buy some mining machines, as many as six, and then you convert them into dollars by you know, converting them into Bitcoins and then transfer them abroad and then boom, you just, you’ve just converted RMB to dollars. But that’s the reason why the vast majority of mining is happening in China is because it’s the cleanest way to convert RMB to dollars and bypass the whole foreign exchange market. So, it’s actually kind of surprising to me that the Chinese government hasn’t clamped down on these miners. I think they kind of liked the idea that the entire Bitcoin system is controlled by Chinese because they’re kind of like, maybe they’re just hanging back and we could mess up this whole Bitcoin thing if we wanted to with just a few phone calls.
Shawn Flynn 43:17
What’s your opinion on the future of Bitcoin?
Gregory LaBlanc 43:19
I think Bitcoins are kind of like the Netscape of crypto. Netscape was amazing. Like Netscape was revolutionary. Netscape changed the world, but who uses Netscape? Nobody, I mean, I think like if you’re selling Angel Dust on the internet, you’re going to use some Bitcoin. If you are trying to do some ransomware on some municipality in Alabama, you’re going to use Bitcoin. So, Bitcoin will always have a market, no doubt. But some of the more practical applications, legal applications of Bitcoin, I’m not so optimistic about it. I mean, it’s a beautiful, beautiful system. It’s a thing of beauty. I mean, it’s a work of art. It’s brilliant. I love it. But it’s just got way too many problems. Seven transactions a second, really, I mean, it serves as a central clearinghouse for all the different exchanges. But at the end of the day, right, if I’m trading on an exchange, I’m not even using Bitcoin. I mean, I’m just… Nothing’s on chain. It’s all just, it’s just a relational database. I’m kind of a Bitcoin pessimist. I can be wrong, again, because it is an amazing technology. It’s really beautiful.
Shawn Flynn 44:22
Now, you’re involved with the FinTech School? What is that and who can benefit from it?
Gregory LaBlanc 44:28
Yeah, so FinTech School is an interesting startup. It’s kind of there to fill a need. You know, a lot of people want to know what’s going on in FinTech. And there’s not a lot of ways to find out about it, other than to read blogs, try and go and find stuff on the internet. And suddenly, just like people are craving insight into programming. And so, we’ve seen the General Assembly, we’ve seen Galvanize, we’ve seen some of these other private companies arise to fill an educational need. FinTech School is there to kind of help educate people, both ordinary people and institutions about what’s happening in the world of FinTech. I mean, in my view, the idea of a FinTech school should probably be one that’s finite. Because I don’t think that term FinTech deserves to last very long because everything is tech. At some point, FinTech will just be finance. In a few years, it won’t be FinTech. They’ll be finance. It won’t be Martech. It will be marketing. There won’t be HR tech. It will be HR, because everything’s tech. If you’re not tech, you’re nothing. You’re just selling counterfeit handbags on the sidewalk. But even those guys probably take WeChat. So, they probably have a good database of buyers, and you know, they take Square, then they’re doing analytics, and you know, whatever. So, the idea of FinTech will soon get absorbed into the broader world of finance.
Shawn Flynn 45:49
And what other information do you think is something that our listeners should know about in this space?
Gregory LaBlanc 45:55
I think investors have to, in general, and this is not just FinTech. But investors have to kind of change the way they value assets. I still meet people that are valuing new companies the way they value old companies, right? They’ll look at a company and they’ll say, “Hey, you know, where’s the profit, right? Or where’s the margin, etc.” And you have to look at it, you know, Amazon was losing money for the first, a long time before they started making money. And yet, you still find people, you know, doing like multiple analysis and all this stuff. It goes way back. But when I was in teaching finance at Duke, many years ago, I had a student come to me and say, “You know, your class conflicts with the golf class. Corporate finance conflicts with golf, like, what do I do? I take golfers. I take corporate finance as a goal you want to. We’re in finance? Yeah, we’ll take the golf class. Because that’s going to help you a lot more than finance. You’re in like, sales and trading, right?”
So, or even in corporate finance, you know, you go out there and golf. Okay, so, so today, if you’re interested in finance, and you come to me and you say, “Hey, should I take the finance class? Or should I take the machine learning class? If I take the finance class, or, better yet the strategy class?” Let’s say take the strategy class. I can’t even begin. Do like DCF on a company. If you don’t understand the competitive landscape, you don’t understand the technology. If you don’t understand the go-to-market, if you don’t understand the customers, you don’t understand substitutes available, the trends, you don’t understand how they’re using data, how they’re using APIs, you know how they’re leveraging platforms, and that’s the raw material for your DCF. It’s like being a chef and not even understanding the wet ingredients, you’re not going to get very far.
Shawn Flynn 47:34
Gregory, this interview has been amazing. Thank you for your time. If anyone wants to get ahold of you, or learn more information about you and what you’re working on, what is the best way to contact you?
Gregory LaBlanc 47:44
Reach out to me on LinkedIn, and tell me like you know who you are, what you’re doing and how you found out about me.
Shawn Flynn 47:50
Great. And, and I also want to thank My at FinTech School who made this introduction that allowed this interview today to happen. We will have his contact information in the show notes along with a link to FinTech School. And Gregory, if it’s okay, I’ll link our LinkedIn. Is that okay? All right. We will get all that taken care of. Once again, Greg, My, everyone, thank you for making this episode happen. Thank you.
Gregory LaBlanc 48:13
Alright. Thanks, Shawn.
Outro 48:14 Thank you for listening to The Silicon Valley Podcast. To access our resources, visit us at TheSiliconValleyPodcast.com and follow our host on Twitter, Facebook, and LinkedIn @ShawnFlynnSV. This show is for entertainment purposes only and is licensed by The Investors Podcast Network. Before making any decisions, consult a professional.
